{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "np.random.seed(42) # fixing the seed\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import rampy as rp\n",
    "from scipy.stats import norm\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML classificator class\n",
    "\n",
    "Below is the class I am working on, to wrap the scikit-learn algorithm in a similar way compared to the regression class:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fake data generation\n",
    "\n",
    "Below we generate 5 fake signals that can be measured modulo some noise. More complex examples can be generated if wanted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples = 100\n",
      "Number of labels = 100\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# The X axis\n",
    "x = np.arange(0, 1000, 1.0)\n",
    "\n",
    "# The perfect 5 signals\n",
    "spectra_1 = rp.gaussian(x, 10.0, 300., 25.) + rp.lorentzian(x, 15., 650., 50.)\n",
    "spectra_2 = rp.gaussian(x, 20.0, 350., 25.) + rp.gaussian(x, 25.0, 380., 20.) + rp.lorentzian(x, 15., 630., 50.)\n",
    "spectra_3 = rp.gaussian(x, 10.0, 500., 50.) + rp.lorentzian(x, 15.0, 520., 10.) + rp.gaussian(x, 25., 530., 3.)\n",
    "spectra_4 = rp.gaussian(x, 10.0, 100., 5.) + rp.lorentzian(x, 30.0, 110., 3.) + rp.gaussian(x, 5., 900., 10.)\n",
    "spectra_5 = rp.gaussian(x, 10.0, 600., 200.)\n",
    "\n",
    "# the number of observations of each signal\n",
    "number_of_spectra = 20\n",
    "\n",
    "# generating a dataset (will be shuffled later during the train-test split)\n",
    "dataset = np.hstack((np.ones((len(x),number_of_spectra))*spectra_1.reshape(-1,1),\n",
    "                     np.ones((len(x),number_of_spectra))*spectra_2.reshape(-1,1),\n",
    "                     np.ones((len(x),number_of_spectra))*spectra_3.reshape(-1,1),\n",
    "                     np.ones((len(x),number_of_spectra))*spectra_4.reshape(-1,1),\n",
    "                     np.ones((len(x),number_of_spectra))*spectra_5.reshape(-1,1)\n",
    "                    )).T\n",
    "\n",
    "# add noise\n",
    "noise_scale = 2.0\n",
    "dataset = dataset + np.random.normal(scale=noise_scale,size=(len(dataset),len(x)))\n",
    "\n",
    "# create numeric labels\n",
    "labels =  np.vstack((np.tile(np.array([1]).reshape(-1,1),number_of_spectra),\n",
    "                     np.tile(np.array([2]).reshape(-1,1),number_of_spectra),\n",
    "                     np.tile(np.array([3]).reshape(-1,1),number_of_spectra),\n",
    "                     np.tile(np.array([4]).reshape(-1,1),number_of_spectra),\n",
    "                     np.tile(np.array([5]).reshape(-1,1),number_of_spectra),\n",
    "                    )).reshape(-1,1)\n",
    "\n",
    "print('Number of samples = {}'.format(dataset.shape[0]))\n",
    "print('Number of labels = {}'.format(labels.shape[0]))\n",
    "\n",
    "# Do figure\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('(a) The 5 \"perfect\" signals')\n",
    "plt.plot(x, spectra_1, label='signal 1')\n",
    "plt.plot(x, spectra_2, label='signal 2')\n",
    "plt.plot(x, spectra_3, label='signal 3')\n",
    "plt.plot(x, spectra_4, label='signal 4')\n",
    "plt.plot(x, spectra_5, label='signal 5')\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Ideal signals')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.title('(b) Noisy observations')\n",
    "plt.plot(x, dataset[0*number_of_spectra:1*number_of_spectra,:].T, color=\"C0\",alpha=0.1)\n",
    "plt.plot(x, dataset[1*number_of_spectra:2*number_of_spectra,:].T, color=\"C1\",alpha=0.1)\n",
    "plt.plot(x, dataset[2*number_of_spectra:3*number_of_spectra,:].T, color=\"C2\",alpha=0.1)\n",
    "plt.plot(x, dataset[3*number_of_spectra:4*number_of_spectra,:].T, color=\"C3\",alpha=0.1)\n",
    "plt.plot(x, dataset[4*number_of_spectra:5*number_of_spectra,:].T, color=\"C4\",alpha=0.1)\n",
    "\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Noisy observed signals (1000 times each)')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning example of treatment\n",
    "\n",
    "This is using directly the scikit-learn functions\n",
    "\n",
    "Note that we do the train-test split here, but mlclassificator can do it too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset\n",
    "y = labels\n",
    "\n",
    "# shufling\n",
    "from sklearn.utils import shuffle\n",
    "X, y = shuffle(X, y, random_state=42)\n",
    "\n",
    "#\n",
    "# TRain/Test split\n",
    "#\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ML classification with rampy\n",
    "\n",
    "This does the exact same thing as above, using the rampy class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- Classifier:Nearest Neighbors is scoring (test:) 1.0.\n",
      "\n",
      "- Classifier:Linear SVM is scoring (test:) 1.0.\n",
      "\n",
      "- Classifier:RBF SVM is scoring (test:) 0.09090909090909091.\n",
      "\n",
      "- Classifier:Gaussian Process is scoring (test:) 0.09090909090909091.\n",
      "\n",
      "- Classifier:Decision Tree is scoring (test:) 0.8787878787878788.\n",
      "\n",
      "- Classifier:Random Forest is scoring (test:) 0.8484848484848485.\n",
      "\n",
      "- Classifier:Neural Net is scoring (test:) 1.0.\n",
      "\n",
      "- Classifier:AdaBoost is scoring (test:) 1.0.\n",
      "\n",
      "- Classifier:Naive Bayes is scoring (test:) 1.0.\n",
      "\n",
      "- Classifier:QDA is scoring (test:) 0.5454545454545454.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/charles/anaconda3/envs/ml/lib/python3.7/site-packages/sklearn/discriminant_analysis.py:715: UserWarning: Variables are collinear\n",
      "  warnings.warn(\"Variables are collinear\")\n"
     ]
    }
   ],
   "source": [
    "# initiate model\n",
    "MLC = rp.mlclassificator(X,y,scaling=False,test_size=0.33)\n",
    "\n",
    "# The algorithms\n",
    "names = [\"Nearest Neighbors\", \"Linear SVM\", \"RBF SVM\", \"Gaussian Process\",\n",
    "         \"Decision Tree\", \"Random Forest\", \"Neural Net\", \"AdaBoost\",\n",
    "         \"Naive Bayes\", \"QDA\"]\n",
    "\n",
    "\n",
    "# iterate over classifiers\n",
    "for name in names:\n",
    "    MLC.algorithm = name\n",
    "    MLC.fit()\n",
    "    score = MLC.model.score(MLC.X_test, MLC.y_test)\n",
    "    print('\\n- Classifier:'+name+' is scoring (test:) '+str(score)+'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This is the code in sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import sklearn\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.datasets import make_moons, make_circles, make_classification\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.gaussian_process.kernels import RBF\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "#\n",
    "# Initiate classifiers\n",
    "#\n",
    "\n",
    "classifiers = [\n",
    "    KNeighborsClassifier(3),\n",
    "    SVC(kernel=\"linear\", C=0.025),\n",
    "    SVC(gamma=2, C=1),\n",
    "    GaussianProcessClassifier(),\n",
    "    DecisionTreeClassifier(max_depth=15),\n",
    "    RandomForestClassifier(max_depth=15, n_estimators=5, max_features=2),\n",
    "    MLPClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GaussianNB(), QuadraticDiscriminantAnalysis()]\n",
    "\n",
    "# iterate over classifiers\n",
    "for name, clf in zip(names, classifiers):\n",
    "    clf.fit(X_train, y_train.ravel())\n",
    "    score = clf.score(X_test, y_test.ravel())\n",
    "    print('\\n- Classifier:'+name+' is scoring '+str(score)+'.')"
   ]
  }
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